Papers by Nicolò Penzo

2 papers
Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations (2024.emnlp-main)

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Challenge: Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs.
Approach: They propose a methodological pipeline to investigate model performance across structural attributes of conversations.
Outcome: The proposed method analyzes the performance of an LLM to classify multi-party conversations . it shows that response selection relies more on the textual content of conversations compared to addressee recognition .
Putting Context in Context: the Impact of Discussion Structure on Text Classification (2024.eacl-long)

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Challenge: Current text classification approaches focus on the content to be classified, but contextual information is neglected in many cases.
Approach: They propose to integrate contextual information into a transformer-based model by feeding it as natural language input into . they also experiment with different amounts of training data and analyse local discussion networks in a privacy-compliant way.
Outcome: The proposed model can be generalized to other datasets and is privacy-compliant.

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